Description Usage Arguments Details Value Author(s) References See Also Examples

This method is deprecated. Use normalize instead. This method implements some normalization approaches for ChIP-seq data after counting reads within regions or bins. Similar methods are often applied to RNA-seq data after counting reads within genes.

1 | ```
normalizeChIP(object, method)
``` |

`object` |
A |

`method` |
Normalization method, either "scaleTotal", "scaleRegion", "scaleMedianRegion" or "quantile" |

The following normalization methods are implemented:

scaleTotalSamples are scaled by a factor such that all samples have the same number of reads (the median number of reads observed accross all samples before normalization). All reads are used for calculating the scaling factor.

scaleRegionSamples are scaled by a factor such that all samples have the same number of reads (the median number of reads observed accross all samples before normalization). In contrast to scaleTotal, only reads falling into the regions (genes, promoters) that were used to create the given

`ChIPseqSet`

object are used for calculating the scaling factor. Hence, the sum of all columns of the returned`ChIPseqSet`

are equal after applying this method.scaleMedianRegionThe scaling factor

*s_j*for the*j*-th sample is defined as:*s_j = median_i \frac{k_{ij}}{∏_{v=1}^m k_{iv}}**k_{ij}*is the value of region*i*in sample*j*. See Anders and Huber (2010) for details.quantileQuantile normalization is applied to the ChIP-seq values such that each sample has the same cdf after normalization.

An `ChIPseqSet-class`

object with normalized ChIP-seq
values.

Hans-Ulrich Klein ([email protected]muenster.de

Anders and Huber; Differential expression analysis for sequence count data; Genome Biology 2010, 11:R106

1 2 3 4 5 6 7 8 9 10 11 12 | ```
chip <- matrix(c(5,6,5,6,10,12,10,12), nrow=4,
dimnames=list(c("f1", "f2", "f3", "f4"), c("s1", "s2")))
rowRanges <- GRanges(IRanges(start=c(10, 20, 30, 40), end=c(11, 21, 31, 41)),
seqnames=c("1", "1", "1", "1"))
names(rowRanges) = rownames(chip)
chipDf <- DataFrame(totalCount=c(100, 100),
row.names=colnames(chip))
cSet <- ChIPseqSet(chipVals=chip, rowRanges=rowRanges, colData=chipDf)
chipVals(cSet)
chipVals(normalize(cSet, method="scaleMedianRegion"))
chipVals(normalize(cSet, method="quantile"))
``` |

epigenomix documentation built on May 6, 2019, 2:17 a.m.

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